spatial-perception

SPARGen introduces a unified multimodal framework that casts 3D reconstruction, dense correspondence, and spatial reasoning as instruction-conditioned generation tasks, enabling shared spatial representations.

This paper introduces masked boundary modeling, a self-supervised paradigm for vision pretraining that learns sub-pixel boundary representations to improve dense spatial perception. The resulting model, LingBot-Vision, demonstrates significant improvements in depth estimation and other downstream tasks, showing that boundary modeling is a scalable pretraining principle for spatially structured visual representations.